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Multi-view Human Pose and Shape Estimation Using Learnable Volumetric Aggregation.
Soyong Shin,Eni Halilaj +1 more
TL;DR: This paper proposes a learnable volumetric aggregation approach to reconstruct 3D human body pose and shape from calibrated multi-view images using a parametric representation of the human body, which makes the approach directly applicable to medical applications.
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Abstract: Human pose and shape estimation from RGB images is a highly sought after alternative to marker-based motion capture, which is laborious, requires expensive equipment, and constrains capture to laboratory environments. Monocular vision-based algorithms, however, still suffer from rotational ambiguities and are not ready for translation in healthcare applications, where high accuracy is paramount. While fusion of data from multiple viewpoints could overcome these challenges, current algorithms require further improvement to obtain clinically acceptable accuracies. In this paper, we propose a learnable volumetric aggregation approach to reconstruct 3D human body pose and shape from calibrated multi-view images. We use a parametric representation of the human body, which makes our approach directly applicable to medical applications. Compared to previous approaches, our framework shows higher accuracy and greater promise for real-time prediction, given its cost efficiency.
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Citations
Multi-View Large Population Gait Database With Human Meshes and Its Performance Evaluation
TL;DR: Experimental results show that the proposed framework estimates human mesh models more accurately than similar methods, providing models of sufficient quality to improve the recognition performance of a baseline model-based gait recognition approach.
•Proceedings Article
End-to-End Model-Based Gait Recognition Using Synchronized Multi-View Pose Constraint
Xiang Li,Yasushi Makihara,Chi Xu,Yasushi Yagi +3 more
- 01 Jan 2021
Learnable Human Mesh Triangulation for 3D Human Pose and Shape Estimation
01 Jan 2023
TL;DR: Zhang et al. as mentioned in this paper proposed a two-stage method, which first estimates the coordinates of mesh vertices through a CNN-based model from input images, and then acquires SMPL parameters by fitting the SMPL model to the estimated vertices.
Delving Deep into Pixel Alignment Feature for Accurate Multi-view Human Mesh Recovery
Hongwen Zhang,Liang An,Yebin Liu +2 more
- 15 Jan 2023
TL;DR: PaFF as mentioned in this paper is an iterative regression framework that performs feature extraction and fusion alternately, extracting pixel-aligned feedback features from each input view according to the reprojection of the current estimation and fusing them together with respect to each vertex of the downsampled mesh.
Delving Deep into Pixel Alignment Feature for Accurate Multi-View Human Mesh Recovery
TL;DR: PaFF as discussed by the authors is an iterative regression framework that performs feature extraction and fusion alternately, extracting pixel-aligned feedback features from each input view according to the reprojection of the current estimation and fusing them together with respect to each vertex of the downsampled mesh.
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OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields
TL;DR: OpenPose as mentioned in this paper uses Part Affinity Fields (PAFs) to learn to associate body parts with individuals in the image, which achieves high accuracy and real-time performance.
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